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A Simple Model for Now-Casting Volatility Series

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  • BREITUNG, Jörg

    (University of Cologne)

  • HAFNER, Christian

    (Université catholique de Louvain, CORE, Belgium)

Abstract

Popular volatility models focus on the conditional variance given past observations, whereas the (arguably most important) information in the current observation is ignored. This paper proposes a simple model for now-casting volatilities based on a specific ARMA representation of the log-transformed squared returns that allows us to estimate current volatility as a function of current and past returns. The model can be viewed as a stochastic volatility model with perfect correlation between the two error terms. It is shown that the volatility nowcasts are invariant to this correlation and therefore the estimated volatilities coincide. An extension of our now-casting model is proposed that takes into account the so-called leverage effect. The alternative models are applied to estimate daily return volatilities from the S&P 500 stock price index.

Suggested Citation

  • BREITUNG, Jörg & HAFNER, Christian, 2016. "A Simple Model for Now-Casting Volatility Series," LIDAM Discussion Papers CORE 2016004, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:2016004
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    References listed on IDEAS

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    Cited by:

    1. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    2. Kejin Wu & Sayar Karmakar, 2023. "A model-free approach to do long-term volatility forecasting and its variants," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-38, December.
    3. Ding, Yashuang (Dexter), 2023. "A simple joint model for returns, volatility and volatility of volatility," Journal of Econometrics, Elsevier, vol. 232(2), pages 521-543.
    4. Kelvin Mutum, 2020. "Volatility Forecast Incorporating Investors’ Sentiment and its Application in Options Trading Strategies: A Behavioural Finance Approach at Nifty 50 Index," Vision, , vol. 24(2), pages 217-227, June.
    5. Ding, Y., 2021. "Augmented Real-Time GARCH: A Joint Model for Returns, Volatility and Volatility of Volatility," Cambridge Working Papers in Economics 2112, Faculty of Economics, University of Cambridge.

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    More about this item

    Keywords

    EGARCH; stochastic volatility; ARMA; realized volatility; leverage;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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